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Enregistrement W2999018115 · doi:10.22605/rrh5640

Establishing a mentorship program in rural workplaces: connection, communication, and support required

2020· article· en· W2999018115 sur OpenAlexafffundabout
Noelle Rohatinsky, Janelle Cave, Chantal Krauter

Notice bibliographique

RevueRural and Remote Health · 2020
Typearticle
Langueen
DomainePsychology
ThématiqueMentoring and Academic Development
Établissements canadiensUniversity of Saskatchewan
Organismes subventionnairesSaskatchewan Health Research Foundation
Mots-clésMentorshipStaffingThematic analysisPromotion (chess)Medical educationNursingMedicinePsychologyPublic relationsQualitative researchPolitical scienceSociology

Résumé

récupéré en direct d'OpenAlex

INTRODUCTION: Recruitment and retention of healthcare providers to rural workplaces is often challenging due to many factors, such as complex work environments requiring a broad skill set, minimal staffing, and limited community support and resources. Mentorship has been proposed as a strategy to encourage recruitment and retention of staff in rural workplaces. This article describes a rural-specific pilot mentorship program that was implemented and evaluated in terms of supporting rural mentorships, easing workplace transition, strengthening community connections, and encouraging recruitment and retention in rural communities. METHODS: Thirty volunteer registered nurse mentors and mentees were recruited from within a western Canadian province. These individuals worked in communities with populations of less than 10 000. Mentors and mentees were matched by program coordinators based on self-identified relationship priorities and similar responses to questions including preferred frequency and method of contact. Online orientation to the program was provided and the formal mentorship lasted 4 months. Follow-up program evaluation was conducted via informal electronic feedback and comprehensive interviews that were analyzed using thematic analysis. RESULTS: Three themes were identified by participants that serve as key considerations when implementing a rural mentorship program: connection, communication, and support. Connection describes the variety of relationships participants formed throughout the mentorship program, including connections to their mentor/mentee, themselves, their profession, colleagues, and the larger rural community. Communication includes the logistics of corresponding between mentee-mentor dyads during the program, participant communication with the coordinators of the program, and future communication about and promotion of rural mentorship programs. Support was described as interpersonal and professional assistance provided to the mentee from the mentor as well as to the mentor from the mentorship program and management. Data from the study suggest that rural-specific mentorships are effective in terms of supporting mentorships, easing workplace transition, strengthening community connections, and encouraging recruitment and retention of registered nurses in rural health care. Pervasive throughout the themes derived from the thematic analysis of interview data was the pivotal role of four key groups (mentors, mentees, the healthcare organization, and the rural community) in developing, facilitating, and sustaining mentorships in rural areas. CONCLUSION: Participants in this study believed that mentorship was beneficial to support healthcare providers working in rural environments. However, greater strides need to be made in terms of creating and supporting such relationships. The responsibility for mentorship resides with not only the mentor and mentee but also health organizations and rural communities. Members from all groups need to be committed and contribute to mentorship for rural mentorship programs to be successful and sustainable. Rural residents are often underserved due to insufficient numbers of healthcare professionals working in rural areas along with a limited number of services offered. The greater the numbers of healthcare professionals that can be recruited and retained within rural communities, the greater the likelihood the community residents will have timely and appropriate access to quality health services. These services can result in positive patient outcomes and greater community health.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,012
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,007
Score d'incertitude au seuil0,033

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,012
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0040,001
Communication savante0,0020,001
Science ouverte0,0010,004
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0070,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,047
Tête enseignante GPT0,347
Écart entre enseignants0,300 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations23
Publié2020
Routes d'admission3
Résumé présentoui

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